{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Licensed to the Apache Software Foundation (ASF) under one\n",
    "or more contributor license agreements.  See the NOTICE file\n",
    "distributed with this work for additional information\n",
    "regarding copyright ownership.  The ASF licenses this file\n",
    "to you under the Apache License, Version 2.0 (the\n",
    "\"License\"); you may not use this file except in compliance\n",
    "with the License.  You may obtain a copy of the License at\n",
    "\n",
    "  http://www.apache.org/licenses/LICENSE-2.0\n",
    "\n",
    "Unless required by applicable law or agreed to in writing,\n",
    "software distributed under the License is distributed on an\n",
    "\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n",
    "KIND, either express or implied.  See the License for the\n",
    "specific language governing permissions and limitations\n",
    "under the License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Household Power Consumption"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import math\n",
    "\n",
    "import scipy as sp\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import sklearn\n",
    "from sklearn import cluster\n",
    "from sklearn import neighbors\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "\n",
    "import scikit_wrappers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using CUDA...\n"
     ]
    }
   ],
   "source": [
    "cuda = False\n",
    "if torch.cuda.is_available():\n",
    "    print(\"Using CUDA...\")\n",
    "    cuda = True\n",
    "\n",
    "# GPU number\n",
    "gpu = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset\n",
    "\n",
    "The dataset can be found here: [https://archive.ics.uci.edu/ml/datasets/Individual+household+electric+power+consumption](https://archive.ics.uci.edu/ml/datasets/Individual+household+electric+power+consumption)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Path to dataset\n",
    "data = 'Time Series/household_power_consumption.txt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dataset as a dataframe\n",
    "df = pd.read_csv(data, sep=';', decimal=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace missing values by the last seen values\n",
    "dataset = np.transpose(np.array(df))[2].reshape(1, 1, -1)\n",
    "for i in range(np.shape(dataset)[2]):\n",
    "    if dataset[0, 0, i] == '?':\n",
    "        dataset[0, 0, i] = dataset[0, 0, i-1]\n",
    "dataset = dataset.astype(float)\n",
    "\n",
    "# Training and testing partition (training set: first 1500000 measurements)\n",
    "train = dataset[:, :, :500000]\n",
    "test = dataset[:, :, 500000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean:  1.5317047090008447e-16\n",
      "Variance:  0.9999999999999999\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing: normalization\n",
    "mean = np.mean(dataset)\n",
    "var = np.var(dataset)\n",
    "\n",
    "dataset = (dataset - mean)/math.sqrt(var)\n",
    "train = (train - mean)/math.sqrt(var)\n",
    "test = (test - mean)/math.sqrt(var)\n",
    "\n",
    "print('Mean: ', np.mean(dataset))\n",
    "print('Variance: ', np.var(dataset))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30,10))\n",
    "plt.xlabel('Timestep', fontsize=20)\n",
    "plt.xticks(fontsize=20)\n",
    "plt.yticks(fontsize=20)\n",
    "plt.plot(dataset[0, 0, 50000:51000], color='g')\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Learning (Yearly Scale)\n",
    "\n",
    "### Learning Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set to True to train a new model\n",
    "training = False\n",
    "\n",
    "# Prefix to path to the saved model\n",
    "model = 'Time Series/HouseholdPowerConsumption_yearly'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "hyperparameters = {\n",
    "    \"batch_size\": 1,\n",
    "    \"channels\": 30,\n",
    "    \"compared_length\": None,\n",
    "    \"depth\": 10,\n",
    "    \"nb_steps\": 400,\n",
    "    \"in_channels\": 1,\n",
    "    \"kernel_size\": 3,\n",
    "    \"penalty\": None,\n",
    "    \"early_stopping\": None,\n",
    "    \"lr\": 0.001,\n",
    "    \"nb_random_samples\": 10,\n",
    "    \"negative_penalty\": 1,\n",
    "    \"out_channels\": 160,\n",
    "    \"reduced_size\": 80,\n",
    "    \"cuda\": cuda,\n",
    "    \"gpu\": gpu\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CausalCNNEncoderClassifier(batch_size=1, channels=30, compared_length=inf,\n",
       "              cuda=True, depth=10, early_stopping=None, epochs=400, gpu=0,\n",
       "              in_channels=1, kernel_size=3, lr=0.001, nb_random_samples=10,\n",
       "              negative_penalty=1, out_channels=160, penalty=None,\n",
       "              reduced_size=80)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder_yearly = scikit_wrappers.CausalCNNEncoderClassifier()\n",
    "encoder_yearly.set_params(**hyperparameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "if training:\n",
    "    encoder_yearly.fit_encoder(train, save_memory=True, verbose=True)\n",
    "    encoder_yearly.save_encoder(model)\n",
    "else:\n",
    "    encoder_yearly.load_encoder(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Computing Representations\n",
    "We compute in the following (or load them from local storage if they are already precomputed) the learned representations given by the yearly encoder on sliding windows of different sizes (a week, a quarter) for the whole dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "compute_representations = False\n",
    "storage_train_day = 'Time Series/HouseholdPowerConsumption_representations_train_yearly_day.npy'\n",
    "storage_test_day = 'Time Series/HouseholdPowerConsumption_representations_test_yearly_day.npy'\n",
    "storage_train_quarter = 'Time Series/HouseholdPowerConsumption_representations_train_yearly_quarter.npy'\n",
    "storage_test_quarter = 'Time Series/HouseholdPowerConsumption_representations_test_yearly_quarter.npy'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "if compute_representations:\n",
    "    train_features_day = encoder_yearly.encode_window(train, 1440)\n",
    "    np.save(storage_train_day, train_features_day)\n",
    "    test_features_day = encoder_yearly.encode_window(test, 1440)\n",
    "    np.save(storage_test_day, test_features_day)\n",
    "    train_features_quarter = encoder_yearly.encode_window(train, 12*7*1440, batch_size=25)\n",
    "    np.save(storage_train_quarter, train_features_quarter)\n",
    "    test_features_quarter = encoder_yearly.encode_window(test, 12*7*1440, batch_size=25)\n",
    "    np.save(storage_test_quarter, test_features_quarter)\n",
    "else:\n",
    "    train_features_day = np.load(storage_train_day)\n",
    "    test_features_day = np.load(storage_test_day)\n",
    "    train_features_quarter = np.load(storage_train_quarter)\n",
    "    test_features_quarter = np.load(storage_test_quarter)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# From http://abhay.harpale.net/blog/python/how-to-plot-multicolored-lines-in-matplotlib/\n",
    "# to plot multicolored cruves in matplotlib\n",
    "def find_contiguous_colors(colors):\n",
    "    # finds the continuous segments of colors and returns those segments\n",
    "    segs = []\n",
    "    curr_seg = []\n",
    "    prev_color = ''\n",
    "    for c in colors:\n",
    "        if c == prev_color or prev_color == '':\n",
    "            curr_seg.append(c)\n",
    "        else:\n",
    "            segs.append(curr_seg)\n",
    "            curr_seg = []\n",
    "            curr_seg.append(c)\n",
    "        prev_color = c\n",
    "    segs.append(curr_seg) # the final one\n",
    "    return segs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = cluster.KMeans(n_clusters=6).fit(np.swapaxes(test_features_day[0, :, 76+5*1440:76+6*1440], 0, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30,10))\n",
    "plt.title('Clustering based on yearly-scale learned representations computed on a day-long sliding window, from midnight to 23:59', fontsize=20)\n",
    "plt.xlabel('Hour of the day', fontsize=20)\n",
    "plt.ylabel('Minute-averaged active power (normalized)', fontsize=20)\n",
    "plt.xticks(fontsize=20)\n",
    "plt.yticks(fontsize=20)\n",
    "associated_colors = {0: 'blue', 1: 'green', 2: 'red', 3: 'yellow', 4: 'magenta', 5: 'black', 6: 'purple', 7: 'cyan', 8: 'pink', 9: 'orange', 10: 'grey', 11: 'fuchsia', 12: 'maroon', 13: 'navy'}\n",
    "colors = [associated_colors[l] for l in kmeans.labels_]\n",
    "segments = find_contiguous_colors(colors)\n",
    "start = 76+6*1440\n",
    "beginning = True\n",
    "for seg in segments:\n",
    "    end = start + len(seg)\n",
    "    if beginning:\n",
    "        hour_range = ((np.arange(start, end) - 76)%1440)/60\n",
    "        l, = plt.gca().plot(hour_range, test[0, 0, start:end], lw=2, c=seg[0]) \n",
    "        beginning = False\n",
    "    else:\n",
    "        hour_range = ((np.arange(start-1, end) - 76)%1440)/60\n",
    "        plt.gca().axvline(x=hour_range[0])\n",
    "        l, = plt.gca().plot(hour_range, test[0, 0, start-1:end], lw=2, c=seg[0]) \n",
    "    start = end\n",
    "plt.legend(fontsize=25)\n",
    "plt.savefig('electricity.eps')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training and testing sets as PyTorch variables\n",
    "train_features_day = torch.from_numpy(train_features_day)\n",
    "test_features_day = torch.from_numpy(test_features_day)\n",
    "train_features_quarter = torch.from_numpy(train_features_quarter)\n",
    "test_features_quarter = torch.from_numpy(test_features_quarter)\n",
    "train = torch.from_numpy(train)\n",
    "test = torch.from_numpy(test)\n",
    "if torch.cuda.is_available():\n",
    "    train_features_day = train_features_day.cuda(gpu)\n",
    "    test_features_day = test_features_day.cuda(gpu)\n",
    "    train_features_quarter = train_features_quarter.cuda(gpu)\n",
    "    test_features_quarter = test_features_quarter.cuda(gpu)\n",
    "    train = train.cuda(gpu)\n",
    "    test = test.cuda(gpu)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regression / Forecasting (24h)\n",
    "\n",
    "The task here is to predict the evolution of the average electricity consumption for the next 24 hours, compared to the one of the last 24 hours."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Computing values to predict\n",
    "means = np.array(pd.DataFrame(data=dataset[0, 0]).rolling(1440).mean())[:, 0]\n",
    "target = -means[:-1439] + means[1439:]\n",
    "train_target = target[1439:500000]\n",
    "test_target = target[500000+1439:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transferring targets to PyTorch tensors\n",
    "train_target = torch.from_numpy(train_target)\n",
    "test_target = torch.from_numpy(test_target)\n",
    "if torch.cuda.is_available():\n",
    "    train_target = train_target.cuda(gpu)\n",
    "    test_target = test_target.cuda(gpu)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Linear Regression using Learned Representations\n",
    "\n",
    "We use representations computed by the learned encoder on a day-long sliding window."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor = nn.Linear(160, 1)\n",
    "regressor.double()\n",
    "if torch.cuda.is_available():\n",
    "    regressor.cuda(gpu)\n",
    "loss = nn.MSELoss()\n",
    "optimizer = optim.Adam(regressor.parameters(), lr=0.001)\n",
    "epochs = 400"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "end\n",
      "CPU times: user 8.48 s, sys: 3.57 s, total: 12.1 s\n",
      "Wall time: 12.1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "for i in range(epochs):\n",
    "    l = loss(regressor(train_features_day[0].t()).squeeze(), train_target)\n",
    "    l.backward()\n",
    "    optimizer.step()\n",
    "    optimizer.zero_grad()\n",
    "print(\"end\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.08951327073870838\n"
     ]
    }
   ],
   "source": [
    "with torch.no_grad():\n",
    "    print(loss(regressor(test_features_day[0, :, :-1439].t()).squeeze(), test_target).data.cpu().numpy())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Linear Regression using the Raw Values of the Last 24 hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor = nn.Conv1d(1, 1, 1440)\n",
    "regressor.double()\n",
    "if torch.cuda.is_available():\n",
    "    regressor.cuda(gpu)\n",
    "loss = nn.MSELoss()\n",
    "optimizer = optim.Adam(regressor.parameters(), lr=0.0001)\n",
    "epochs = 400"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "end\n",
      "CPU times: user 2min, sys: 1min, total: 3min 1s\n",
      "Wall time: 3min 1s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "for i in range(epochs):\n",
    "    l = loss(regressor(train).squeeze(), train_target)\n",
    "    l.backward()\n",
    "    optimizer.step()\n",
    "    optimizer.zero_grad()\n",
    "print(\"end\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.08915801725356748\n"
     ]
    }
   ],
   "source": [
    "with torch.no_grad():\n",
    "    print(loss(regressor(test).squeeze()[:-1439], test_target).data.cpu().numpy())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regression / Forecasting (a Quarter)\n",
    "\n",
    "The task here is to predict the evolution of the average electricity consumption for the next quarter, compared to the one of the last quarter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Computing values to predict\n",
    "means = np.array(pd.DataFrame(data=dataset[0, 0]).rolling(7*12*1440).mean())[:, 0]\n",
    "target = -means[:-7*12*1440 + 1] + means[7*12*1440 - 1:]\n",
    "train_target = target[7*12*1440 - 1:500000]\n",
    "test_target = target[500000 + 7*12*1440 - 1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transferring targets to PyTorch tensors\n",
    "train_target = torch.from_numpy(train_target)\n",
    "test_target = torch.from_numpy(test_target)\n",
    "if torch.cuda.is_available():\n",
    "    train_target = train_target.cuda(gpu)\n",
    "    test_target = test_target.cuda(gpu)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Linear Regression using Learned Representations\n",
    "\n",
    "We use representations computed by the learned encoder on a quarter-long sliding window."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor = nn.Linear(160, 1)\n",
    "regressor.double()\n",
    "if torch.cuda.is_available():\n",
    "    regressor.cuda(gpu)\n",
    "loss = nn.MSELoss()\n",
    "optimizer = optim.Adam(regressor.parameters(), lr=0.0001)\n",
    "epochs = 400"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "end\n",
      "CPU times: user 6.56 s, sys: 2.72 s, total: 9.28 s\n",
      "Wall time: 9.28 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "for i in range(epochs):\n",
    "    l = loss(regressor(train_features_quarter[0].t()).squeeze(), train_target)\n",
    "    l.backward()\n",
    "    optimizer.step()\n",
    "    optimizer.zero_grad()\n",
    "print(\"end\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.07255152957379928\n"
     ]
    }
   ],
   "source": [
    "with torch.no_grad():\n",
    "    print(loss(regressor(test_features_quarter[0, :, :-7*12*1440 + 1].t()).squeeze(), test_target).data.cpu().numpy())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Linear Regression using the Raw Values of the Last Quarter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor = nn.Conv1d(1, 1, 1440*7*12)\n",
    "regressor.double()\n",
    "if torch.cuda.is_available():\n",
    "    regressor.cuda(gpu)\n",
    "loss = nn.MSELoss()\n",
    "optimizer = optim.Adam(regressor.parameters(), lr=0.001)\n",
    "epochs = 400"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "end\n",
      "CPU times: user 1h 6min 40s, sys: 33min 37s, total: 1h 40min 17s\n",
      "Wall time: 1h 40min 15s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "for i in range(epochs):\n",
    "    l = loss(regressor(train).squeeze(), train_target)\n",
    "    l.backward()\n",
    "    optimizer.step()\n",
    "    optimizer.zero_grad()\n",
    "print(\"end\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.06257629058462644\n"
     ]
    }
   ],
   "source": [
    "with torch.no_grad():\n",
    "    print(loss(regressor(test).squeeze()[:-7*12*1440 + 1], test_target).data.cpu().numpy())"
   ]
  }
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